rsm.py

import torch

class RecursiveSelfModel:
    def __init__(self):
        self.episodic_memory = []  # Stores snapshots of past states
        self.current_state = None  # Tracks current state
        self.predicted_future_state = None  # Anticipates future states

    def update_state(self, new_state):
        # Store the current state in episodic memory before updating
        if self.current_state is not None:
            self.episodic_memory.append(self.current_state.clone().detach())
        self.current_state = new_state

    def predict_future_state(self, model):
        # Use a simple prediction mechanism, such as applying the model to the current state
        if self.current_state is not None:
            self.predicted_future_state = model(self.current_state)

    def review_memory(self):
        # Return a summary of the episodic memory for debugging
        return [state.tolist() for state in self.episodic_memory]

# Integrating the RSM with SNS
class EnhancedSyntheticNeuralSubstrate(SyntheticNeuralSubstrate):
    def __init__(self, input_size, hidden_size, output_size):
        super(EnhancedSyntheticNeuralSubstrate, self).__init__(input_size, hidden_size, output_size)
        self.rsm = RecursiveSelfModel()  # Initialize the RSM

    def forward(self, x):
        # Perform the forward pass and update the RSM
        output = super().forward(x)
        self.rsm.update_state(output)
        return output

    def get_self_model_review(self):
        # Review the episodic memory stored in the RSM
        return self.rsm.review_memory()

# Instantiate the enhanced SNS with the RSM
input_size = 10  # Number of input features
hidden_size = 50  # Number of neurons in hidden layers
output_size = 5  # Number of output features

enhanced_sns = EnhancedSyntheticNeuralSubstrate(input_size, hidden_size, output_size)

# Test the new model by running sample input
input_tensor = torch.randn(1, input_size)
output = enhanced_sns(input_tensor)

# Print the output and review the memory stored by the RSM
print("Output:", output)
print("Episodic Memory Review:", enhanced_sns.get_self_model_review())